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      Knowledge in Learning &middot; AIMA Exercises 
    
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    <p>Artificial Intelligence : A Modern Approach</p>
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    <a class="sidebar-nav-item" href="/aima-exercises/">Home</a>
    <span class="sidebar-nav-item">Part - I Artificial Intelligence</span>
  <a class="sidebar-nav-item" href="/aima-exercises/intro-exercises/">Chapter 1 - Introduction</a>
  <a class="sidebar-nav-item" href="/aima-exercises/agents-exercises/">Chapter 2 - Intelligent Agents</a>
  <span class="sidebar-nav-item">Part - II Problem Solving</span>
  <a class="sidebar-nav-item" href="/aima-exercises/search-exercises/">Chapter 3 - Solving Problems By Searching</a>
  <a class="sidebar-nav-item" href="/aima-exercises/advanced-search-exercises">Chapter 4 - Beyond Classical Search</a>
  <a class="sidebar-nav-item" href="/aima-exercises/game-playing-exercises">Chapter 5 - Adversarial Search</a>
  <a class="sidebar-nav-item" href="/aima-exercises/csp-exercises">Chapter 6 - Constraint Satisfaction Problems</a>
  <span class="sidebar-nav-item">Part - III Knowledge, Reasoning and Planning</span>
  <a class="sidebar-nav-item" href="/aima-exercises/knowledge-logic-exercises">Chapter 7 - Logical Agents</a>
  <a class="sidebar-nav-item" href="/aima-exercises/fol-exercises">Chapter 8 - First Order Logic</a>
  <a class="sidebar-nav-item" href="/aima-exercises/logical-inference-exercises">Chapter 9 - Inference in First Order Logic</a>
  <a class="sidebar-nav-item" href="/aima-exercises/planning-exercises">Chapter 10 - Classical Planning</a>
  <a class="sidebar-nav-item" href="/aima-exercises/advanced-planning-exercises">Chapter 11 - Planning and Acting in Real Life</a>
  <a class="sidebar-nav-item" href="/aima-exercises/kr-exercises">Chapter 12 - Knowledge Representation</a>
  <span class="sidebar-nav-item">Part - IV Uncertaing Knowledge and Reasoning</span>
  <a class="sidebar-nav-item" href="/aima-exercises/probability-exercises">Chapter 13 - Quantifying Uncertainty</a>
  <a class="sidebar-nav-item" href="/aima-exercises/bayes-nets-exercises">Chapter 14 - Probabilistic Reasoning</a>
  <a class="sidebar-nav-item" href="/aima-exercises/dbn-exercises">Chapter 15 - Probabilistic Reasoning Over Time</a>
  <a class="sidebar-nav-item" href="/aima-exercises/decision-theory-exercises">Chapter 16 - Making-Simple Decisions</a>
  <a class="sidebar-nav-item" href="/aima-exercises/complex-decisions-exercises">Chapter 17 - Making Complex Decisions</a>
  <span class="sidebar-nav-item">Part - V Lerning</span>
  <a class="sidebar-nav-item" href="/aima-exercises/concept-learning-exercises">Chapter 18 - Learning From Examples</a>
  <a class="sidebar-nav-item" href="/aima-exercises/ilp-exercises">Chapter 19 - Knowledge In Learning</a>
  <a class="sidebar-nav-item" href="/aima-exercises/bayesian-learning-exercises">Chapter 20 - Learning Probabilistic Models</a>
  <a class="sidebar-nav-item" href="/aima-exercises/reinforcement-learning-exercises">Chapter 21 - Reinforcement Learning</a>
  <span class="sidebar-nav-item">Part - VI Communicating, Perceiving and Acting</span>
  <a class="sidebar-nav-item" href="/aima-exercises/nlp-communicating-exercises">Chapter 22 - Natural Language Processing</a>
  <a class="sidebar-nav-item" href="/aima-exercises/nlp-english-exercises">Chapter 23 - Natural Language For Communication</a>
  <a class="sidebar-nav-item" href="/aima-exercises/perception-exercises">Chapter 24 - Perception</a>
  <a class="sidebar-nav-item" href="/aima-exercises/robotics-exercises">Chapter 25 - Robotics</a>
  <span class="sidebar-nav-item">Part - VII Conclusions</span>
  <a class="sidebar-nav-item" href="/aima-exercises/philosophy-exercises">Chapter 26 - Philosophical Foundations</a>
  <a class="sidebar-nav-item" href="/aima-exercises/#/">Chapter 27 - AI The Present And Future</a>
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<h1 id="19-knowledge-in-learning">19. Knowledge in Learning</h1>

<div class="card">
<div class="card-header p-2">
<a href="ex_1/" class="p-2">Exercise 1 (dbsig-exercise) </a>
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<p class="card-text">

Show, by translating into conjunctive normal form and
applying resolution, that the conclusion drawn on page <a class="pageRef" title="" href="#">dbsig-page</a>
concerning Brazilians is sound.
</p>
</div>
</div>
<p><br /></p>
<div class="card">
<div class="card-header p-2">
<a href="ex_2/" class="p-2">Exercise 2 </a>
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<div class="card-body">
<p class="card-text">

For each of the following determinations, write down the logical
representation and explain why the determination is true (if it is):<br />

1.  Design and denomination determine the mass of a coin.<br />

2.  For a given program, input determines output.<br />

3.  Climate, food intake, exercise, and metabolism determine weight gain
    and loss.<br />

4.  Baldness is determined by the baldness (or lack thereof) of one’s
    maternal grandfather. <br />
</p>
</div>
</div>
<p><br /></p>
<div class="card">
<div class="card-header p-2">
<a href="ex_3/" class="p-2">Exercise 3 </a>
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<div class="card-body">
<p class="card-text">

For each of the following determinations, write down the logical
representation and explain why the determination is true (if it is):<br />

1.  Zip code determines the state (U.S.).<br />

2.  Design and denomination determine the mass of a coin.<br />

3.  Climate, food intake, exercise, and metabolism determine weight gain
    and loss.<br />

4.  Baldness is determined by the baldness (or lack thereof) of one’s
    maternal grandfather.<br />
</p>
</div>
</div>
<p><br /></p>
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<div class="card-header p-2">
<a href="ex_4/" class="p-2">Exercise 4 </a>
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</div>
<div class="card-body">
<p class="card-text">

Would a probabilistic version of determinations be useful? Suggest a
definition.
</p>
</div>
</div>
<p><br /></p>
<div class="card">
<div class="card-header p-2">
<a href="ex_5/" class="p-2">Exercise 5 (ir-step-exercise) </a>
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<div class="card-body">
<p class="card-text">

Fill in the missing values for the clauses $C_1$ or
$C_2$ (or both) in the following sets of clauses, given that $C$ is the
resolvent of $C_1$ and $C_2$:<br />

1.  $C = {True} \Rightarrow P(A,B)$,
    $C_1 = P(x,y) \Rightarrow Q(x,y)$, $C_2
    = ??$.<br />

2.  $C = {True} \Rightarrow P(A,B)$, $C_1 = ??$,
    $C_2 = ??$.<br />

3.  $C = P(x,y) \Rightarrow P(x,f(y))$, $C_1 = ??$,
    $C_2 = ??$.<br />

If there is more than one possible solution, provide one example of each
different kind.<br />
</p>
</div>
</div>
<p><br /></p>
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<a href="ex_6/" class="p-2">Exercise 6 (prolog-ir-exercise) </a>
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<div class="card-body">
<p class="card-text">

Suppose one writes a logic program that carries
out a resolution inference step. That is, let ${Resolve}(c_1,c_2,c)$
succeed if $c$ is the result of resolving $c_1$ and $c_2$. Normally,
${Resolve}$ would be used as part of a theorem prover by calling it
with $c_1$ and $c_2$ instantiated to particular clauses, thereby
generating the resolvent $c$. Now suppose instead that we call it with
$c$ instantiated and $c_1$ and $c_2$ uninstantiated. Will this succeed
in generating the appropriate results of an inverse resolution step?
Would you need any special modifications to the logic programming system
for this to work?
</p>
</div>
</div>
<p><br /></p>
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<a href="ex_7/" class="p-2">Exercise 7 (foil-literals-exercise) </a>
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<div class="card-body">
<p class="card-text">

Suppose that is considering adding a literal
to a clause using a binary predicate $P$ and that previous literals
(including the head of the clause) contain five different variables.<br />

1.  How many functionally different literals can be generated? Two
    literals are functionally identical if they differ only in the names
    of the *new* variables that they contain.<br />

2.  Can you find a general formula for the number of different literals
    with a predicate of arity $r$ when there are $n$ variables
    previously used?<br />

3.  Why does not allow literals that contain no previously used
    variables?<br />
</p>
</div>
</div>
<p><br /></p>
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<a href="ex_8/" class="p-2">Exercise 8 </a>
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<div class="card-body">
<p class="card-text">

Using the data from the family tree in
Figure <a class="insideBookFigRef" target="_blank" href="https://aimacode.github.io/aima-exercises/figures/family2-figure.png">family2-figure</a>, or a subset thereof, apply the
algorithm to learn a definition for the ${Ancestor}$ predicate.
</p>
</div>
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<p><br /></p>

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